| title | Learning Native Reflection in Unified Models with Interleaved Reinforcement Learning |
|---|---|
| authors | Yijia Fan, Ziqi Huang, Zhongang Cai, Yan Li, Zimo Wen, Wanqi Yin, Haiwen Diao, Ziwei Liu |
| date | 2026-09-28 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2609.35767 |
| affiliation | MMLab@NTU |
| upvotes | 46 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | Unified multimodal models can both look at and render images, so in principle they can repair their own generations: diagnose what an image gets wrong, revise it, observe the result, and diagnose again. Whether a revision helps is known only after it is rendered, so the reflection text and the image generation must be learned jointly, over the whole loop. Supervised fine-tuning (SFT) on reflection trajectories gives a cold start but does not find the high-success repair paths, and naive RL that optimizes only the renderer or only one head leaves most of the gain untapped. We introduce UMM-Reflection, which applies reinforcement learning (RL) to complete reflection trajectories inside one unified model: sibling trajectories share one initial image, so the group-relative advantage compares reflection strategies, and one trajectory-level advantage updates both the reflection tokens and the flow-based revisions, avoiding the combinatorial blow-up of per-round credit assignment. Unlike single-round editing or pipelines with an external critic, credit flows across rounds and to both roles of the same model, and no verifier is needed at inference. On BAGEL, UMM-Reflection improves GenEval by 12.05 points over SFT, and the gains transfer to WISE (+10.97), OneIG-Bench (+3.48), and T2I-CompBench++ (+4.63), none of which is used in training. |
cite
@misc{fan2026learning,
title={Learning Native Reflection in Unified Models with Interleaved Reinforcement Learning},
author={Yijia Fan and Ziqi Huang and Zhongang Cai and Yan Li and Zimo Wen and Wanqi Yin and Haiwen Diao and Ziwei Liu},
year={2026},
eprint={2609.35767},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2609.35767}
}